Application of Laser Spectroscopy and Machine Learning for Diagnostics of Uncontrolled Type 2 Diabetes.

IF 2.2 3区 化学 Q2 INSTRUMENTS & INSTRUMENTATION
Imran Rehan, Kamran Rehan, Sabiha Sultana, Mujeeb Ur Rehman
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引用次数: 0

Abstract

Diabetes, a chronic metabolic disorder affecting millions worldwide, presents a persistent need for reliable and non-invasive diagnostic techniques. Here, we suggest a highly effective approach for differentiating between fingernails from diabetic individuals and those from healthy controls using laser-induced breakdown spectroscopy (LIBS). The excitation source employed was a Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser emitting light with a wavelength of 1064  nm. The initial differentiation between individuals with and without diabetes was achieved by applying principal component analysis (PCA) to LIBS spectral data, which was then incorporated into a novel machine-learning model. The classification model designed for a non-invasive system included random forest (RF), an extreme learning machine (ELM) classifier, and a hybrid classification model incorporating cross-validation techniques to evaluate the outcomes. The algorithm analyses the complete spectrum of both healthy and diseased samples, categorizing them according to differences in LIBS spectral intensity. The classification performance of the model was assessed using a k-fold cross-validation method. Seven parameters, i.e., specificity, sensitivity, area under curve (AUC), accuracy, precision, recall, and F-score, were used to evaluate the model's overall performance. The findings affirmed that the suggested non-invasive model could predict diabetic diseases with an accuracy of 95%.

激光光谱学和机器学习在未控制2型糖尿病诊断中的应用。
糖尿病是一种影响全球数百万人的慢性代谢紊乱,对可靠和非侵入性诊断技术的需求持续存在。在这里,我们提出了一种非常有效的方法来区分糖尿病患者的指甲和健康人的指甲,使用激光诱导击穿光谱(LIBS)。激发源为调q掺钕钇铝石榴石(Nd:YAG)激光器,发射波长为1064 nm。通过将主成分分析(PCA)应用于LIBS光谱数据,实现了糖尿病患者和非糖尿病患者之间的初步区分,然后将其纳入新的机器学习模型。为非侵入性系统设计的分类模型包括随机森林(RF)、极限学习机(ELM)分类器和结合交叉验证技术的混合分类模型来评估结果。该算法分析健康和患病样本的完整光谱,根据LIBS光谱强度的差异对它们进行分类。使用k-fold交叉验证方法评估模型的分类性能。采用特异性、敏感性、曲线下面积(AUC)、准确度、精密度、召回率和f分数等7个参数评价模型的整体性能。研究结果证实,所建议的无创模型预测糖尿病疾病的准确率为95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Spectroscopy
Applied Spectroscopy 工程技术-光谱学
CiteScore
6.60
自引率
5.70%
发文量
139
审稿时长
3.5 months
期刊介绍: Applied Spectroscopy is one of the world''s leading spectroscopy journals, publishing high-quality peer-reviewed articles, both fundamental and applied, covering all aspects of spectroscopy. Established in 1951, the journal is owned by the Society for Applied Spectroscopy and is published monthly. The journal is dedicated to fulfilling the mission of the Society to “…advance and disseminate knowledge and information concerning the art and science of spectroscopy and other allied sciences.”
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